Extended Data Figure 9
rstudio
September 11, 2024 14:15:30 CEST
Introduction
Load libraries
library(Seurat)
library(canceRbits)
library(dplyr)
library(patchwork)
library(DT)
library(SCpubr)
library(tibble)
library(dittoSeq)
library(scRepertoire)
library(reshape2)
library(viridis)
library(tidyr)
library(grDevices)
library(ggpubr)
library(ggplot2)
library(ggnewscale)
library(RColorBrewer)
library(scales)
library(enrichplot)
library(clusterProfiler)
library("org.Hs.eg.db")
library(DOSE)
library(msigdbr)
library(stringr)Load single cell RNA-Seq data
# Load seurat object containing single cell pre-processed and annotated data, in the metadata folder
srat <- readRDS(params$path_to_data)
meta <- srat@meta.data
meta$WHO <- "SD"
meta$WHO[meta$patient %in% c("NeoBCC007_post", "NeoBCC008_post", "NeoBCC012_post", "NeoBCC017_post")] <- "CR"
meta$WHO[meta$patient %in% c("NeoBCC004_post", "NeoBCC006_post", "NeoBCC010_post", "NeoBCC011_post")] <- "PR"
srat <- AddMetaData(srat, meta$WHO, col.name = "WHO")
srat$WHO <- factor(srat$WHO, levels = c("CR", "PR", "SD"))colors
colors_B <- c(
"34" = "#ae017e",
"7" = "#377eb8",
"21" = "#f781bf",
"38" = "#fed976",
"15" = "#a6cee3" ,
"22" = "#e31a1c" ,
"3" = "#9e9ac8",
"4" = "#fdb462" ,
"24" = "#b3de69"
)
colors_clonotype = c("Small (1e-04 < X <= 0.001)" = "#8c6bb1",
"Medium (0.001 < X <= 0.01)" = "#41b6c4",
"Large (0.01 < X <= 0.1)" = "#fec44f",
"Hyperexpanded (0.1 < X <= 1)" = "#ce1256"
)Extended Data Figure 9a
df <- s@meta.data %>%
mutate_if(sapply(s@meta.data, is.character), as.factor) %>%
group_by( Pathological.Response, patient, seurat_clusters, .drop = FALSE)%>%
summarise(Nb = n()) %>%
mutate(C = sum(Nb)) %>%
mutate(percent = Nb/C*100) %>%
filter(percent != "NaN") %>%
arrange(Pathological.Response, percent)
tmp <- df[df$seurat_clusters %in% c("34"),]
p1 <- ggpaired(tmp,
x = "Pathological.Response",
y = "percent",
id="patient",
color = "black",
fill = "Pathological.Response" ,
palette= c("white", "#dd3497", "#ae017e"),
point.size = 2,
xlab = "",
ylab = "Percentage (%)") +
ylim(c(0,90)) +
ggtitle("C34") + NoLegend() +
stat_compare_means( method = "wilcox.test",
paired = FALSE,
label.y = 80,
hide.ns = FALSE,
label = "p.format",
label.x.npc = "center",
bracket.size = 1,
comparison = list(c("pCR", "non-pCR")),
size = 8) +
theme(text = element_text(size = 18),axis.text.x = element_text(angle = 90, vjust=0.5))
tmp <- df[df$seurat_clusters %in% c("7"),]
p2 <- ggpaired(tmp,
x = "Pathological.Response",
y = "percent",
id="patient",
color = "black",
fill = "Pathological.Response" ,
palette= c("white", "#1d91c0", "#253494"),
point.size = 2,
xlab = "",
ylab = "") +
ylim(c(0,90)) +
ggtitle("C7") + NoLegend() +
stat_compare_means( method = "wilcox.test",
paired = FALSE,
label.y = 80,
hide.ns = FALSE,
label = "p.format",
label.x.npc = "center",
bracket.size = 1,
comparison = list(c("pCR", "non-pCR")),
size = 8) +
theme(text = element_text(size = 18),axis.text.x = element_text(angle = 90, vjust=0.5))
tmp <- df[df$seurat_clusters %in% c("21"),]
p3 <- ggpaired(tmp,
x = "Pathological.Response",
y = "percent",
id="patient",
color = "black",
fill = "Pathological.Response" ,
palette= c("white", "#fde0dd", "#fa9fb5"),
point.size = 2,
xlab = "",
ylab = "") +
ylim(c(0,90)) +
ggtitle("C21") + NoLegend() +
stat_compare_means( method = "wilcox.test",
paired = FALSE,
label.y = 80,
hide.ns = FALSE,
label = "p.format",
label.x.npc = "center",
bracket.size = 1,
comparison = list(c("pCR", "non-pCR")),
size = 8) +
theme(text = element_text(size = 18),axis.text.x = element_text(angle = 90, vjust=0.5))
tmp <- df[df$seurat_clusters %in% c("38"),]
p4 <- ggpaired(tmp,
x = "Pathological.Response",
y = "percent",
id="patient",
color = "black",
fill = "Pathological.Response" ,
palette= c("white", "#ffffcc", "yellow"),
point.size = 2,
xlab = "",
ylab = "") +
ylim(c(0,90)) +
ggtitle("C38") + NoLegend() +
stat_compare_means( method = "wilcox.test",
paired = FALSE,
label.y = 80,
hide.ns = FALSE,
label = "p.format",
label.x.npc = "center",
bracket.size = 1,
comparison = list(c("pCR", "non-pCR")),
size = 8) +
theme(text = element_text(size = 18),axis.text.x = element_text(angle = 90, vjust=0.5))
tmp <- df[df$seurat_clusters %in% c("15"),]
p5 <- ggpaired(tmp,
x = "Pathological.Response",
y = "percent",
id="patient",
color = "black",
fill = "Pathological.Response" ,
palette= c("white", "#deebf7", "#9ecae1"),
point.size = 2,
xlab = "",
ylab = "") +
ylim(c(0,90)) +
ggtitle("C15") + NoLegend() +
stat_compare_means( method = "wilcox.test",
paired = FALSE,
label.y = 80,
hide.ns = FALSE,
label = "p.format",
label.x.npc = "center",
bracket.size = 1,
comparison = list(c("pCR", "non-pCR")),
size = 8) +
theme(text = element_text(size = 18),axis.text.x = element_text(angle = 90, vjust=0.5))
tmp <- df[df$seurat_clusters %in% c("22"),]
p6 <- ggpaired(tmp,
x = "Pathological.Response",
y = "percent",
id="patient",
color = "black",
fill = "Pathological.Response" ,
palette= c("white", "#fc9272", "#ef3b2c"),
point.size = 2,
xlab = "",
ylab = "") +
ylim(c(0,90)) +
ggtitle("C22") + NoLegend() +
stat_compare_means( method = "wilcox.test",
paired = FALSE,
label.y = 80,
hide.ns = FALSE,
label = "p.format",
label.x.npc = "center",
bracket.size = 1,
comparison = list(c("pCR", "non-pCR")),
size = 8) +
theme(text = element_text(size = 18),axis.text.x = element_text(angle = 90, vjust=0.5))
tmp <- df[df$seurat_clusters %in% c("3"),]
p7 <- ggpaired(tmp,
x = "Pathological.Response",
y = "percent",
id="patient",
color = "black",
fill = "Pathological.Response" ,
palette= c("white", "#decbe4", "#c2a5cf"),
point.size = 2,
xlab = "",
ylab = "") +
ylim(c(0,90)) +
ggtitle("C3") + NoLegend() +
stat_compare_means( method = "wilcox.test",
paired = FALSE,
label.y = 80,
hide.ns = FALSE,
label = "p.format",
label.x.npc = "center",
bracket.size = 1,
comparison = list(c("pCR", "non-pCR")),
size = 8) +
theme(text = element_text(size = 18),axis.text.x = element_text(angle = 90, vjust=0.5))
tmp <- df[df$seurat_clusters %in% c("4"),]
p8 <- ggpaired(tmp,
x = "Pathological.Response",
y = "percent",
id="patient",
color = "black",
fill = "Pathological.Response" ,
palette= c("white", "#fee0b6", "#fec44f"),
point.size = 2,
xlab = "",
ylab = "") +
ylim(c(0,90)) +
ggtitle("C4") + NoLegend() +
stat_compare_means( method = "wilcox.test",
paired = FALSE,
label.y = 80,
hide.ns = FALSE,
label = "p.format",
label.x.npc = "center",
bracket.size = 1,
comparison = list(c("pCR", "non-pCR")),
size = 8) +
theme(text = element_text(size = 18),axis.text.x = element_text(angle = 90, vjust=0.5))
tmp <- df[df$seurat_clusters %in% c("24"),]
p9 <- ggpaired(tmp,
x = "Pathological.Response",
y = "percent",
id="patient",
color = "black",
fill = "Pathological.Response" ,
palette= c("white", "#b8e186", "#7fbc41"),
point.size = 2,
xlab = "",
ylab = "") +
ylim(c(0,90)) +
ggtitle("C24") + NoLegend() +
stat_compare_means( method = "wilcox.test",
paired = FALSE,
label.y = 80,
hide.ns = FALSE,
label = "p.format",
label.x.npc = "center",
bracket.size = 1,
comparison = list(c("pCR", "non-pCR")),
size = 8) +
theme(text = element_text(size = 18),axis.text.x = element_text(angle = 90, vjust=0.5))
p <- p1 + p2 + p3 + p4 + p5 + p6 + p7 + p8 + p9 + plot_layout(nrow = 1, guides = "collect")
p
DT::datatable(rbind(p1$data, p2$data, p3$data, p4$data, p5$data, p6$data, p7$data, p8$data, p9$data),
caption = ("Extended Data Figure 9A"),
extensions = 'Buttons',
options = list( dom = 'Bfrtip',
buttons = c( 'csv', 'excel')))s$WHO <- factor(s$WHO, levels = c("CR","PR","SD"))
df <- s@meta.data %>%
mutate_if(sapply(s@meta.data, is.character), as.factor) %>%
group_by( WHO, patient, seurat_clusters, .drop = FALSE)%>%
summarise(Nb = n()) %>%
mutate(C = sum(Nb)) %>%
mutate(percent = Nb/C*100) %>%
filter(percent != "NaN") %>%
arrange(WHO, percent)
tmp <- df[df$seurat_clusters %in% c("34"),]
p1 <- ggpaired(tmp,
x = "WHO",
y = "percent",
id="patient",
color = "black",
fill = "WHO" ,
palette= c("white", "#dd3497", "#ae017e"),
point.size = 2,
xlab = "",
ylab = "Percentage (%)") +
ylim(c(0,90)) +
ggtitle("C34") + NoLegend() +
stat_compare_means( method = "wilcox.test",
paired = FALSE,
label.y = 80,
hide.ns = FALSE,
label = "p.signif",
label.x.npc = "center",
bracket.size = 1,
comparison = list(c("CR", "SD"), c("SD", "PR"), c("CR", "PR")),
size = 8) +
theme(text = element_text(size = 18),axis.text.x = element_text(angle = 90, vjust=0.5))
tmp <- df[df$seurat_clusters %in% c("7"),]
p2 <- ggpaired(tmp,
x = "WHO",
y = "percent",
id="patient",
color = "black",
fill = "WHO" ,
palette= c("white", "#1d91c0", "#253494"),
point.size = 2,
xlab = "",
ylab = "") +
ylim(c(0,90)) +
ggtitle("C7") + NoLegend() +
stat_compare_means( method = "wilcox.test",
paired = FALSE,
label.y = 80,
hide.ns = FALSE,
label = "p.signif",
label.x.npc = "center",
bracket.size = 1,
comparison = list(c("CR", "SD"), c("SD", "PR"), c("CR", "PR")),
size = 8) +
theme(text = element_text(size = 18),axis.text.x = element_text(angle = 90, vjust=0.5))
tmp <- df[df$seurat_clusters %in% c("21"),]
p3 <- ggpaired(tmp,
x = "WHO",
y = "percent",
id="patient",
color = "black",
fill = "WHO" ,
palette= c("white", "#fde0dd", "#fa9fb5"),
point.size = 2,
xlab = "",
ylab = "") +
ylim(c(0,90)) +
ggtitle("C21") + NoLegend() +
stat_compare_means( method = "wilcox.test",
paired = FALSE,
label.y = 80,
hide.ns = FALSE,
label = "p.signif",
label.x.npc = "center",
bracket.size = 1,
comparison = list(c("CR", "SD"), c("SD", "PR"), c("CR", "PR")),
size = 8) +
theme(text = element_text(size = 18),axis.text.x = element_text(angle = 90, vjust=0.5))
tmp <- df[df$seurat_clusters %in% c("38"),]
p4 <- ggpaired(tmp,
x = "WHO",
y = "percent",
id="patient",
color = "black",
fill = "WHO" ,
palette= c("white", "#ffffcc", "yellow"),
point.size = 2,
xlab = "",
ylab = "") +
ylim(c(0,90)) +
ggtitle("C38") + NoLegend() +
stat_compare_means( method = "wilcox.test",
paired = FALSE,
label.y = 80,
hide.ns = FALSE,
label = "p.signif",
label.x.npc = "center",
bracket.size = 1,
comparison = list(c("CR", "SD"), c("SD", "PR"), c("CR", "PR")),
size = 8) +
theme(text = element_text(size = 18),axis.text.x = element_text(angle = 90, vjust=0.5))
tmp <- df[df$seurat_clusters %in% c("15"),]
p5 <- ggpaired(tmp,
x = "WHO",
y = "percent",
id="patient",
color = "black",
fill = "WHO" ,
palette= c("white", "#deebf7", "#9ecae1"),
point.size = 2,
xlab = "",
ylab = "") +
ylim(c(0,90)) +
ggtitle("C15") + NoLegend() +
stat_compare_means( method = "wilcox.test",
paired = FALSE,
label.y = 80,
hide.ns = FALSE,
label = "p.signif",
label.x.npc = "center",
bracket.size = 1,
comparison = list(c("CR", "SD"), c("SD", "PR"), c("CR", "PR")),
size = 8) +
theme(text = element_text(size = 18),axis.text.x = element_text(angle = 90, vjust=0.5))
tmp <- df[df$seurat_clusters %in% c("22"),]
p6 <- ggpaired(tmp,
x = "WHO",
y = "percent",
id="patient",
color = "black",
fill = "WHO" ,
palette= c("white", "#fc9272", "#ef3b2c"),
point.size = 2,
xlab = "",
ylab = "") +
ylim(c(0,90)) +
ggtitle("C22") + NoLegend() +
stat_compare_means( method = "wilcox.test",
paired = FALSE,
label.y = 80,
hide.ns = FALSE,
label = "p.signif",
label.x.npc = "center",
bracket.size = 1,
comparison = list(c("CR", "SD"), c("SD", "PR"), c("CR", "PR")),
size = 8) +
theme(text = element_text(size = 18),axis.text.x = element_text(angle = 90, vjust=0.5))
tmp <- df[df$seurat_clusters %in% c("3"),]
p7 <- ggpaired(tmp,
x = "WHO",
y = "percent",
id="patient",
color = "black",
fill = "WHO" ,
palette= c("white", "#decbe4", "#c2a5cf"),
point.size = 2,
xlab = "",
ylab = "") +
ylim(c(0,90)) +
ggtitle("C3") + NoLegend() +
stat_compare_means( method = "wilcox.test",
paired = FALSE,
label.y = 80,
hide.ns = FALSE,
label = "p.signif",
label.x.npc = "center",
bracket.size = 1,
comparison = list(c("CR", "SD"), c("SD", "PR"), c("CR", "PR")),
size = 8) +
theme(text = element_text(size = 18),axis.text.x = element_text(angle = 90, vjust=0.5))
tmp <- df[df$seurat_clusters %in% c("4"),]
p8 <- ggpaired(tmp,
x = "WHO",
y = "percent",
id="patient",
color = "black",
fill = "WHO" ,
palette= c("white", "#fee0b6", "#fec44f"),
point.size = 2,
xlab = "",
ylab = "") +
ylim(c(0,90)) +
ggtitle("C4") + NoLegend() +
stat_compare_means( method = "wilcox.test",
paired = FALSE,
label.y = 80,
hide.ns = FALSE,
label = "p.signif",
label.x.npc = "center",
bracket.size = 1,
comparison = list(c("CR", "SD"), c("SD", "PR"), c("CR", "PR")),
size = 8) +
theme(text = element_text(size = 18),axis.text.x = element_text(angle = 90, vjust=0.5))
tmp <- df[df$seurat_clusters %in% c("24"),]
p9 <- ggpaired(tmp,
x = "WHO",
y = "percent",
id="patient",
color = "black",
fill = "WHO" ,
palette= c("white", "#b8e186", "#7fbc41"),
point.size = 2,
xlab = "",
ylab = "") +
ylim(c(0,90)) +
ggtitle("C24") + NoLegend() +
stat_compare_means( method = "wilcox.test",
paired = FALSE,
label.y = 80,
hide.ns = FALSE,
label = "p.signif",
label.x.npc = "center",
bracket.size = 1,
comparison = list(c("CR", "SD"), c("SD", "PR"), c("CR", "PR")),
size = 8) +
theme(text = element_text(size = 18),axis.text.x = element_text(angle = 90, vjust=0.5))
p <- p1 + p2 + p3 + p4 + p5 + p6 + p7 + p8 + p9 + plot_layout(nrow = 1, guides = "collect")
p
Extended Data Figure 9b
p1 <- Seurat::VlnPlot(s,
features = "HLA-DRA",
group.by = "seurat_clusters",
pt.size = 0,
cols = colors_B) & NoLegend()
p1 <- p1 + geom_boxplot(width=0.3, fill="white")
f1 <- SCpubr::do_FeaturePlot(s,
features = "HLA-DRA", legend.position = "right", legend.length = 4)
p2 <- Seurat::VlnPlot(s,
features = "GZMK",
group.by = "seurat_clusters",
pt.size = 0,
cols = colors_B) & NoLegend()
p2 <- p2 + geom_boxplot(width=0.3, fill="white")
f2 <- SCpubr::do_FeaturePlot(s,
features = "GZMK", legend.position = "right", legend.length = 4)
p3 <- Seurat::VlnPlot(s,
features = "CD27",
group.by = "seurat_clusters",
pt.size = 0,
cols = colors_B) & NoLegend()
p3 <- p3 + geom_boxplot(width=0.3, fill="white")
f3 <- SCpubr::do_FeaturePlot(s,
features = "CD27", legend.position = "right", legend.length = 4)
p4 <- Seurat::VlnPlot(s,
features = "XBP1",
group.by = "seurat_clusters",
pt.size = 0,
cols = colors_B) & NoLegend()
p4 <- p4 + geom_boxplot(width=0.3, fill="white")
f4 <- SCpubr::do_FeaturePlot(s,
features = "XBP1", legend.position = "right", legend.length = 4)
c1 <- f1+p1 + plot_layout(ncol = 2, widths = c(1,2))
c2 <- f2+p2 + plot_layout(ncol = 2, widths = c(1,2))
c3 <- f3+p3 + plot_layout(ncol = 2, widths = c(1,2))
c4 <- f4+p4 + plot_layout(ncol = 2, widths = c(1,2))
c1
DT::datatable(f1$data,
caption = ("EDFigure 9Ba_FeaturePlot"),
extensions = 'Buttons',
options = list( dom = 'Bfrtip',
buttons = c( 'csv', 'excel')))
DT::datatable(f2$data,
caption = ("EDFigure 9Bb_FeaturePlot"),
extensions = 'Buttons',
options = list( dom = 'Bfrtip',
buttons = c( 'csv', 'excel')))
DT::datatable(f3$data,
caption = ("EDFigure 9Bc_FeaturePlot"),
extensions = 'Buttons',
options = list( dom = 'Bfrtip',
buttons = c( 'csv', 'excel')))
DT::datatable(f4$data,
caption = ("EDFigure 9Bd_FeaturePlot"),
extensions = 'Buttons',
options = list( dom = 'Bfrtip',
buttons = c( 'csv', 'excel')))DT::datatable(p1$data,
caption = ("EDFigure 9Ba_VlnPlot"),
extensions = 'Buttons',
options = list( dom = 'Bfrtip',
buttons = c( 'csv', 'excel')))DT::datatable(p2$data,
caption = ("EDFigure 9Bb_VlnPlot"),
extensions = 'Buttons',
options = list( dom = 'Bfrtip',
buttons = c( 'csv', 'excel')))Extended Data Figure 9C
p1 <- Seurat::VlnPlot(s,
features = "PRF1",
group.by = "seurat_clusters",
pt.size = 0,
cols = colors_B) & NoLegend()
p1 <- p1 + geom_boxplot(width=0.3, fill="white")
f1 <- SCpubr::do_FeaturePlot(s,
features = "PRF1", legend.position = "right", legend.length = 4)
p2 <- Seurat::VlnPlot(s,
features = "IL10",
group.by = "seurat_clusters",
pt.size = 0,
cols = colors_B) & NoLegend()
p2 <- p2 + geom_boxplot(width=0.3, fill="white")
f2 <- SCpubr::do_FeaturePlot(s,
features = "IL10", legend.position = "right", legend.length = 4)
p3 <- Seurat::VlnPlot(s,
features = "TGFB1",
group.by = "seurat_clusters",
pt.size = 0,
cols = colors_B) & NoLegend()
p3 <- p3 + geom_boxplot(width=0.3, fill="white")
f3 <- SCpubr::do_FeaturePlot(s,
features = "TGFB1", legend.position = "right", legend.length = 4)
c1 <- f1+p1 + plot_layout(ncol = 1, heights = c(1,1))
c2 <- f2+p2 + plot_layout(ncol = 1, heights = c(1,1))
c3 <- f3+p3 + plot_layout(ncol = 1, heights = c(1,1))
c1
DT::datatable(f1$data,
caption = ("ExtendedData_Figure9Ca_FeaturePlot"),
extensions = 'Buttons',
options = list( dom = 'Bfrtip',
buttons = c( 'csv', 'excel')))DT::datatable(p1$data,
caption = ("ExtendedData_Figure9Ca_VlnPlot"),
extensions = 'Buttons',
options = list( dom = 'Bfrtip',
buttons = c( 'csv', 'excel')))
DT::datatable(f2$data,
caption = ("ExtendedData_Figure9Cb_FeaturePlot"),
extensions = 'Buttons',
options = list( dom = 'Bfrtip',
buttons = c( 'csv', 'excel')))DT::datatable(p2$data,
caption = ("ExtendedData_Figure9Cb_VlnPlot"),
extensions = 'Buttons',
options = list( dom = 'Bfrtip',
buttons = c( 'csv', 'excel')))
Session Info
## R version 4.3.0 (2023-04-21)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.4 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: Europe/Vienna
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] stringr_1.5.1 msigdbr_7.5.1 DOSE_3.26.2 org.Hs.eg.db_3.17.0
## [5] AnnotationDbi_1.62.2 IRanges_2.34.1 S4Vectors_0.38.2 Biobase_2.60.0
## [9] BiocGenerics_0.46.0 clusterProfiler_4.8.3 enrichplot_1.20.3 scales_1.3.0
## [13] RColorBrewer_1.1-3 ggnewscale_0.4.10 tidyr_1.3.1 scRepertoire_1.10.1
## [17] dittoSeq_1.12.2 canceRbits_0.1.6 ggpubr_0.6.0.999 ggplot2_3.5.1
## [21] viridis_0.6.5 viridisLite_0.4.2 reshape2_1.4.4 tibble_3.2.1
## [25] SCpubr_2.0.2 DT_0.32 patchwork_1.2.0 dplyr_1.1.4
## [29] Seurat_5.0.3 SeuratObject_5.0.1 sp_2.1-3
##
## loaded via a namespace (and not attached):
## [1] fs_1.6.4 matrixStats_1.2.0 spatstat.sparse_3.0-3
## [4] bitops_1.0-7 HDO.db_0.99.1 httr_1.4.7
## [7] doParallel_1.0.17 tools_4.3.0 sctransform_0.4.1
## [10] backports_1.4.1 utf8_1.2.4 R6_2.5.1
## [13] vegan_2.6-4 lazyeval_0.2.2 uwot_0.1.16
## [16] mgcv_1.9-1 permute_0.9-7 withr_3.0.0
## [19] gridExtra_2.3 progressr_0.14.0 cli_3.6.2
## [22] spatstat.explore_3.2-7 fastDummies_1.7.3 scatterpie_0.2.1
## [25] isoband_0.2.7 labeling_0.4.3 sass_0.4.9
## [28] spatstat.data_3.0-4 ggridges_0.5.6 pbapply_1.7-2
## [31] yulab.utils_0.1.4 gson_0.1.0 stringdist_0.9.12
## [34] parallelly_1.37.1 limma_3.56.2 RSQLite_2.3.5
## [37] VGAM_1.1-10 rstudioapi_0.16.0 generics_0.1.3
## [40] gridGraphics_0.5-1 ica_1.0-3 spatstat.random_3.2-3
## [43] crosstalk_1.2.1 car_3.1-2 GO.db_3.17.0
## [46] Matrix_1.6-5 ggbeeswarm_0.7.2 fansi_1.0.6
## [49] abind_1.4-5 lifecycle_1.0.4 edgeR_3.42.4
## [52] yaml_2.3.8 carData_3.0-5 SummarizedExperiment_1.30.2
## [55] qvalue_2.32.0 Rtsne_0.17 blob_1.2.4
## [58] grid_4.3.0 promises_1.2.1 crayon_1.5.2
## [61] miniUI_0.1.1.1 lattice_0.22-6 cowplot_1.1.3
## [64] KEGGREST_1.40.1 pillar_1.9.0 knitr_1.45
## [67] fgsea_1.26.0 GenomicRanges_1.52.1 future.apply_1.11.1
## [70] codetools_0.2-19 fastmatch_1.1-4 leiden_0.4.3.1
## [73] glue_1.7.0 downloader_0.4 ggfun_0.1.5
## [76] data.table_1.15.2 treeio_1.24.3 vctrs_0.6.5
## [79] png_0.1-8 spam_2.10-0 gtable_0.3.5
## [82] assertthat_0.2.1 cachem_1.1.0 xfun_0.43
## [85] S4Arrays_1.0.6 mime_0.12 tidygraph_1.3.1
## [88] survival_3.5-8 DElegate_1.2.1 SingleCellExperiment_1.22.0
## [91] pheatmap_1.0.12 iterators_1.0.14 fitdistrplus_1.1-11
## [94] ROCR_1.0-11 nlme_3.1-164 ggtree_3.13.0.001
## [97] bit64_4.0.5 RcppAnnoy_0.0.22 evd_2.3-6.1
## [100] GenomeInfoDb_1.36.4 bslib_0.6.2 irlba_2.3.5.1
## [103] vipor_0.4.7 KernSmooth_2.23-22 DBI_1.2.2
## [106] colorspace_2.1-0 ggrastr_1.0.2 tidyselect_1.2.1
## [109] bit_4.0.5 compiler_4.3.0 SparseM_1.81
## [112] DelayedArray_0.26.7 plotly_4.10.4 shadowtext_0.1.3
## [115] lmtest_0.9-40 digest_0.6.35 goftest_1.2-3
## [118] spatstat.utils_3.0-4 rmarkdown_2.26 XVector_0.40.0
## [121] htmltools_0.5.8 pkgconfig_2.0.3 sparseMatrixStats_1.12.2
## [124] MatrixGenerics_1.12.3 highr_0.10 fastmap_1.2.0
## [127] rlang_1.1.4 htmlwidgets_1.6.4 shiny_1.8.1
## [130] farver_2.1.2 jquerylib_0.1.4 zoo_1.8-12
## [133] jsonlite_1.8.8 BiocParallel_1.34.2 GOSemSim_2.26.1
## [136] RCurl_1.98-1.14 magrittr_2.0.3 GenomeInfoDbData_1.2.10
## [139] ggplotify_0.1.2 dotCall64_1.1-1 munsell_0.5.1
## [142] Rcpp_1.0.12 evmix_2.12 babelgene_22.9
## [145] ape_5.8 reticulate_1.35.0 truncdist_1.0-2
## [148] stringi_1.8.4 ggalluvial_0.12.5 ggraph_2.2.1
## [151] zlibbioc_1.46.0 MASS_7.3-60.0.1 plyr_1.8.9
## [154] parallel_4.3.0 listenv_0.9.1 ggrepel_0.9.5
## [157] forcats_1.0.0 deldir_2.0-4 Biostrings_2.68.1
## [160] graphlayouts_1.1.1 splines_4.3.0 tensor_1.5
## [163] locfit_1.5-9.9 igraph_2.0.3 spatstat.geom_3.2-9
## [166] cubature_2.1.0 ggsignif_0.6.4 RcppHNSW_0.6.0
## [169] evaluate_0.23 foreach_1.5.2 tweenr_2.0.3
## [172] httpuv_1.6.15 RANN_2.6.1 purrr_1.0.2
## [175] polyclip_1.10-6 future_1.33.2 scattermore_1.2
## [178] ggforce_0.4.2 broom_1.0.5 xtable_1.8-4
## [181] tidytree_0.4.6 RSpectra_0.16-1 rstatix_0.7.2
## [184] later_1.3.2 gsl_2.1-8 aplot_0.2.3
## [187] beeswarm_0.4.0 memoise_2.0.1 cluster_2.1.6
## [190] powerTCR_1.20.0 globals_0.16.3